1. Field of the Invention
The present invention generally relates to the detection of capillaries and small blood vessels in videos recorded from tissue surfaces, such as the lingual surface and, more particularly, to a system and process for quantitative assessment of video signals for detection and characterization of capillaries in order to monitor and assess changes that occur in microcirculation to assist physicians in making diagnostically and therapeutically important decisions such as determination of the effectiveness of the resuscitation process.
2. Background Description
Knowledge of healthy distribution and circulation of blood in capillaries has been considered as a key factor to assess tissue oxygenation (see, for example, V. Cerný, Z. Turek, and R. Parízková, “Orthogonal polarization spectral imaging: a review,” Physiol. Res. 56, 2007). Study of microcirculation has shown potential diagnostic value in diseases such as sepsis (see, for example, R. M. Bateman, M. D. Sharpe, and C. G. Ellis, “Bench-to-bedside review: microvascular dysfunction in sepsis: hemodynamics, oxygen transport and nitric oxide”. Crit Care Med 7: 359-373, 2003), chronic ulcers, diabetes mellitus, and hypertension (see, for example, B. I. Levy, G. Ambrosio, A. R. Pries, and H. A. Struijker-Boudier. “Microcirculation in hypertension: a new target for treatment?”Circulation 104:735-740, 2001, and C. Verdant and D. De Backer, “How monitoring of the microcirculation may help us at the bedside”, Curr Opin Crit Care 2005, 11(3):240-244). The alteration in microcirculation measures during resuscitation is also of interest of numerous physicians (see, for example, Sala Y, Dubois M. J., D. De Backer, J. Creteur, and J. L. Vincent, “Persistent microcirculatory alterations are associated with organ failure and death in patients with sepsis shock”, Crit Care Med 2004, 32:1825-1831, P. E. Spronk, C. Ince, M. J. Gardien, K. R. Mathura, H. M. Oudemans-van Straaten, and D. F. Zandstra, “Nitroglycerin in sepsis shock after intravascular volume resuscitation”, Lancet 2002, 360:1395-1396, and Michael Fries, MD; Weil, Max Harry, MD, PhD, FCCM; Yun-Te Chang, MD; Carlos Castillo, MSEE; Wanchun Tang, MD, FCCM “Microcirculation during cardiac arrest and resuscitation”, Crit Care Med 34 (2006), pp. 445-457). A technology that can quantitatively detect and monitor the changes in microcirculation can lead to early detection of these pathological conditions, and therefore better chance of treatment (see, for example, Orsolya Genzel-Boroviczeny, Julia Strotgen, Anthony G. Harris, Konrad Messmer, and Frank Christ, “Orthogonal polarization spectral imaging (OPS): A novel method to measure the microcirculation in term and preterm infants transcutaneously”, Pediatr Res 51:386-391, 2002). In particular, in trauma, it is highly desirable to automatically monitor microcirculation during resuscitation and decide when to start and/or stop resuscitation according to real-time quantitative analysis of microcirculation.
Recently developed hardware systems have provided the means to capture video recordings of capillaries in lingual surface. In particular, the two major imaging methods, Orthogonal Polarization Spectral (OPS) imaging (see Genzel-Boroziczeny et al., ibid.) and Side-stream Dark Field (SDF) imaging (see Ince C, “The microcirculation is the motor of sepsis”, Critical Care 2005, 9(suppl 4):S13-S19) are being widely employed in the field of clinical microcirculatory research. In this research study, video recordings with high resolution captured by Microscan system were acquired. Despite the advances in the hardware, the lack of effective computational methods to analyze and interpret these images is still the main challenge.
Dobbe et al. proposed a method based on image stabilization, centerline detection and space time diagram (J. G. G. Dobbe, G. J. Streekstra, B. Atasever, R. van Zijderveld and C. Ince, “The measurement of functional microcirculatory density and velocity distributions using automated image analysis”, Med Biol Eng Comput. 2008 July; 46(7): 659-670). Pattern recognition techniques were used by Joes Staal et al. to extract ridges (Joes Staal, Michael D. Abrámoff, Meindert Niemeijer, Max A. Viergever, and Bram van Ginneken, “Ridge-Based vessel segmentation in color images of the retina”, IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501-509, 2004). Hoover and Goldbaum (Adam Hoover and Michael Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels”, IEEE Tran. on Medical Imaging, Vol. 22, No. 8, August 2003, p. 951-958) utilized fuzzy convergence to extract the optic nerve in images of the ocular fundus. Vermeer et al. (K. A. Vermeer, F. M. Vos, H. G. Lemij, A. M. Vossepoel, “A model based method for retinal blood vessel detection”, Comput. Biol. Med., in press. DOI: 10.1016/S0010-4825(03)00055-6, 2003) proposed a model based approach. Artificial intelligence-based approaches were applied by Rost et al. (U. Rost, H. Munkel, and C.-E. Liedtke, “A knowledge based system for the configuration of image processing algorithms”, Fachtagung Informations and Mikrosystem Technik, March, 1998).